Editor's Note: This is the first in a four-part series examining the growing role of machine learning and artificial intelligence in the power sector. Tomorrow, we look at how regional grid operators are using AI to optimize operations. The future of the electric grid is undoubtedly cleaner and more efficient and distributed, with hefty doses of technology and machine learning helping to operate it all. But if you're expecting a system dramatically transformed, experts say you'll be left waiting. Artificial intelligence and machine learning are already helping utilities run their networks more efficiently, extending the life of equipment and helping to dispatch energy into markets more efficiently.

People without any knowledge can use artificial intelligence (AI) to produce more profitable cucumbers than the most experienced growers. This was shown in an experiment that Wageningen University & Research (WUR) in the Netherlands conducted last year in a greenhouse complex at their experimental farm in Bleiswijk. WUR researcher Isabella Righini cited this research at the Global Future Farming Summit, which was held in Wageningen recently. Experts there focused on precision agriculture and the robotisation of food production. Righini: "Producing food requires a lot from the grower. He must deal with the supply of raw materials, such as minerals for his crops. He must take care of the crops. And he has to market his crops. That is a complex operation. In the Autonomous Greenhouse project we are testing to what extent artificial intelligence can help growers grow crops."

In July the poker-playing bot Pluribus beat top professionals in a six-player no-limit Texas Hold'Em poker game. Pluribus taught itself from scratch using a form of reinforcement learning (RL) to become the first AI program to defeat elite humans in a poker game with more than two players. Compared to perfect information games such as Chess or Go, poker presents a number of unique challenges with its concealed cards, bluffing and other human strategies. Now a team of researchers from Texas A&M University and Canada's Simon Fraser University have open-sourced a toolkit called "RLCard" for applying RL research to card games. While RL has already produced a number of breakthroughs in goal-oriented tasks and has high potential, it's not without its drawbacks.

Artificial Intelligence is becoming more common place with enterprises today. Fueled by the popularity and success of applications like Netflix, Amazon, Alexa etc. which use AI technologies to a considerable degree, companies are vying with each other to emerge on top. But there are a number of areas that need to be optimized before the success can be ensured. How do companies ensure that the technology is well-governed, scalable, cost-effective & trust worthy? The answer is having a well-defined strategy in place which can be derived by studying the enterprises who are succeeding with it. We have worked with Fortune 500 enterprises, in defining their strategy and delivering successful & cutting-edge analytics and AI projects.

The use of artificial intelligence (AI) and machine learning is on the rise but it's important to know which reputation management processes should and shouldn't be automated. AI, using natural language processing (NLP) models, allows computers to understand and decipher what a human is saying. This is starting to be used by companies in South Africa responding to customers through online channels. It also being employed increasingly in the reputation marketing sector. The main reasons organisations turn to these technologies is that they dramatically improve efficiency, can reduce errors and they save time.

In the near future, having an appointment with your doctor online will become as normal as making a payment on an app or booking a taxi through Uber. Disruptive technologies like Artificial Intelligence (AI) and telehealth, along with tech giants like Amazon, Apple, and Google, are changing the face of healthcare as we know it. According to the World Health Organisation, global healthcare spending reached $7.5 trillion in 2016, representing 10% of global GDP. Growing and aging populations, as well as an increase in chronic diseases are among some of the biggest contributors to these rising costs and as a result, hospitals and physicians are under immense strain. This is where technological advancements could potentially alleviate some of the problems affecting the healthcare industry, particularly when it comes to creating greater efficiencies across the board.

The introduction of Artificial Intelligence (AI) into the insurance industry is going to transform the sector and change the customer experience forever. Behavioral policy pricing or value-based pricing will provide personalized data to pricing platforms, for example, people with a healthier lifestyle to pay less for health insurance. The omnipresent IoT sensors are providing the data for value-based pricing. They will allow insurance carriers to price coverage based on real events in real-time using massive datasets. Usage-based or pay per mile car insurance can also be cited as an example.

The new Artificial Intelligence and Blockchain Investment Fund was launched earlier this year and will finance the development of highly innovative artificial intelligence and blockchain companies as part of a wider move to create a dynamic EU-wide innovation ecosystem. Run by the European Investment Fund, the first phase of the AI and blockchain fund will make available €100 million in 2020 to support companies working in this sector. More information about how to apply for funding will be available soon, and companies attending the Slush event can learn more about the fund at the European Commission's workshop. Europe is home to a world-leading AI and blockchain research community, with vibrant start-ups and a wide range of traditional industries looking to adopt innovative processes and models based on these technologies. Investment levels in Europe have traditionally lagged those in the US and Asia, however, and the aim of the fund is to narrow this investment gap and support the development of innovation in this field.

Human brains are singularly special in the animal kingdom, writes Andrew Quixley, Data Science and AI Sales Lead, IBM South Africa. We are the curious, communicative collaborators who rose from a simple foraging existence on the savannahs to build structures of incredible complexity. Sure, other animals are curious, communicative and collaborative too. An octopus will investigate and solve problems; elephants use infrasound to communicate over vast distances; termites collaborate to build structures that are millions of times larger than any individual; but none of these feats comes close to the scale of human complexity. And the key to this complexity is our ability to generate language. There are 7 099 official languages on Earth.

"Aren't you two ever going to read Hogwarts, A History?" How many times throughout the Harry Potter series does Hermione bug Harry and Ron to read the enormous tome Hogwarts, A History? Hint: it's a lot. How many nights do the three of them spend in the library, reading through every book they can find to figure out who Nicolas Flamel is, or how to survive underwater, or preparing for their O.W.L.s? The mistake they're all making is to try to read everything themselves. Remember when you were in school and stumbled upon the CliffsNotes summary of that book you never read but were supposed to write an essay about? That's basically what text summarization does: provide the CliffsNotes version for any large document.